Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 MiB
Average record size in memory482.8 B

Variable types

Text1
Numeric14
Categorical11

Alerts

avg_tenure_per_company is highly overall correlated with experience_gap and 2 other fieldsHigh correlation
cluster is highly overall correlated with overtimeHigh correlation
distance_from_home is highly overall correlated with distance_from_home_logHigh correlation
distance_from_home_log is highly overall correlated with distance_from_homeHigh correlation
experience_gap is highly overall correlated with avg_tenure_per_company and 2 other fieldsHigh correlation
num_companies_worked is highly overall correlated with avg_tenure_per_companyHigh correlation
overtime is highly overall correlated with clusterHigh correlation
pca1 is highly overall correlated with years_in_current_roleHigh correlation
pca2 is highly overall correlated with experience_gapHigh correlation
tenure_performance is highly overall correlated with years_at_companyHigh correlation
total_working_years is highly overall correlated with avg_tenure_per_company and 1 other fieldsHigh correlation
years_at_company is highly overall correlated with tenure_performanceHigh correlation
years_in_current_role is highly overall correlated with pca1High correlation
employee_id has unique values Unique
pca1 has unique values Unique
pca2 has unique values Unique
years_at_company has 505 (5.1%) zeros Zeros
years_in_current_role has 643 (6.4%) zeros Zeros
num_companies_worked has 1340 (13.4%) zeros Zeros
training_times_last_year has 1676 (16.8%) zeros Zeros
tenure_performance has 505 (5.1%) zeros Zeros
experience_gap has 256 (2.6%) zeros Zeros

Reproduction

Analysis started2025-04-14 16:55:47.693488
Analysis finished2025-04-14 16:56:04.385376
Duration16.69 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

employee_id
Text

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size634.9 KiB
2025-04-14T22:26:04.658537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowEMP00000
2nd rowEMP00001
3rd rowEMP00002
4th rowEMP00003
5th rowEMP00004
ValueCountFrequency (%)
emp00000 1
 
< 0.1%
emp00008 1
 
< 0.1%
emp00017 1
 
< 0.1%
emp00002 1
 
< 0.1%
emp00003 1
 
< 0.1%
emp00004 1
 
< 0.1%
emp00005 1
 
< 0.1%
emp00006 1
 
< 0.1%
emp00007 1
 
< 0.1%
emp00009 1
 
< 0.1%
Other values (9990) 9990
99.9%
2025-04-14T22:26:05.030273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14000
17.5%
E 10000
12.5%
M 10000
12.5%
P 10000
12.5%
6 4000
 
5.0%
7 4000
 
5.0%
3 4000
 
5.0%
4 4000
 
5.0%
5 4000
 
5.0%
8 4000
 
5.0%
Other values (3) 12000
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14000
17.5%
E 10000
12.5%
M 10000
12.5%
P 10000
12.5%
6 4000
 
5.0%
7 4000
 
5.0%
3 4000
 
5.0%
4 4000
 
5.0%
5 4000
 
5.0%
8 4000
 
5.0%
Other values (3) 12000
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14000
17.5%
E 10000
12.5%
M 10000
12.5%
P 10000
12.5%
6 4000
 
5.0%
7 4000
 
5.0%
3 4000
 
5.0%
4 4000
 
5.0%
5 4000
 
5.0%
8 4000
 
5.0%
Other values (3) 12000
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14000
17.5%
E 10000
12.5%
M 10000
12.5%
P 10000
12.5%
6 4000
 
5.0%
7 4000
 
5.0%
3 4000
 
5.0%
4 4000
 
5.0%
5 4000
 
5.0%
8 4000
 
5.0%
Other values (3) 12000
15.0%

age
Real number (ℝ)

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.5612
Minimum22
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:26:05.125888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile23
Q131
median41
Q350
95-th percentile58
Maximum59
Range37
Interquartile range (IQR)19

Descriptive statistics

Standard deviation10.876483
Coefficient of variation (CV)0.26814993
Kurtosis-1.1770529
Mean40.5612
Median Absolute Deviation (MAD)9
Skewness-0.010169294
Sum405612
Variance118.29788
MonotonicityNot monotonic
2025-04-14T22:26:05.217030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
47 305
 
3.0%
38 292
 
2.9%
34 286
 
2.9%
44 285
 
2.9%
50 282
 
2.8%
49 279
 
2.8%
39 278
 
2.8%
46 276
 
2.8%
37 275
 
2.8%
56 273
 
2.7%
Other values (28) 7169
71.7%
ValueCountFrequency (%)
22 251
2.5%
23 266
2.7%
24 258
2.6%
25 266
2.7%
26 246
2.5%
27 259
2.6%
28 238
2.4%
29 261
2.6%
30 258
2.6%
31 257
2.6%
ValueCountFrequency (%)
59 246
2.5%
58 272
2.7%
57 254
2.5%
56 273
2.7%
55 252
2.5%
54 270
2.7%
53 265
2.6%
52 238
2.4%
51 248
2.5%
50 282
2.8%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size605.5 KiB
Male
4973 
Female
4826 
Other
 
201

Length

Max length6
Median length5
Mean length4.9853
Min length4

Characters and Unicode

Total characters49853
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowOther

Common Values

ValueCountFrequency (%)
Male 4973
49.7%
Female 4826
48.3%
Other 201
 
2.0%

Length

2025-04-14T22:26:05.312357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:26:05.393402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 4973
49.7%
female 4826
48.3%
other 201
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e 14826
29.7%
a 9799
19.7%
l 9799
19.7%
M 4973
 
10.0%
F 4826
 
9.7%
m 4826
 
9.7%
O 201
 
0.4%
t 201
 
0.4%
h 201
 
0.4%
r 201
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49853
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14826
29.7%
a 9799
19.7%
l 9799
19.7%
M 4973
 
10.0%
F 4826
 
9.7%
m 4826
 
9.7%
O 201
 
0.4%
t 201
 
0.4%
h 201
 
0.4%
r 201
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49853
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14826
29.7%
a 9799
19.7%
l 9799
19.7%
M 4973
 
10.0%
F 4826
 
9.7%
m 4826
 
9.7%
O 201
 
0.4%
t 201
 
0.4%
h 201
 
0.4%
r 201
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49853
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14826
29.7%
a 9799
19.7%
l 9799
19.7%
M 4973
 
10.0%
F 4826
 
9.7%
m 4826
 
9.7%
O 201
 
0.4%
t 201
 
0.4%
h 201
 
0.4%
r 201
 
0.4%

marital_status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size625.1 KiB
Single
3406 
Divorced
3371 
Married
3223 

Length

Max length8
Median length7
Mean length6.9965
Min length6

Characters and Unicode

Total characters69965
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDivorced
2nd rowDivorced
3rd rowDivorced
4th rowSingle
5th rowSingle

Common Values

ValueCountFrequency (%)
Single 3406
34.1%
Divorced 3371
33.7%
Married 3223
32.2%

Length

2025-04-14T22:26:05.547156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:26:05.620704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
single 3406
34.1%
divorced 3371
33.7%
married 3223
32.2%

Most occurring characters

ValueCountFrequency (%)
i 10000
14.3%
e 10000
14.3%
r 9817
14.0%
d 6594
9.4%
S 3406
 
4.9%
n 3406
 
4.9%
g 3406
 
4.9%
l 3406
 
4.9%
D 3371
 
4.8%
v 3371
 
4.8%
Other values (4) 13188
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69965
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 10000
14.3%
e 10000
14.3%
r 9817
14.0%
d 6594
9.4%
S 3406
 
4.9%
n 3406
 
4.9%
g 3406
 
4.9%
l 3406
 
4.9%
D 3371
 
4.8%
v 3371
 
4.8%
Other values (4) 13188
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69965
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 10000
14.3%
e 10000
14.3%
r 9817
14.0%
d 6594
9.4%
S 3406
 
4.9%
n 3406
 
4.9%
g 3406
 
4.9%
l 3406
 
4.9%
D 3371
 
4.8%
v 3371
 
4.8%
Other values (4) 13188
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69965
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 10000
14.3%
e 10000
14.3%
r 9817
14.0%
d 6594
9.4%
S 3406
 
4.9%
n 3406
 
4.9%
g 3406
 
4.9%
l 3406
 
4.9%
D 3371
 
4.8%
v 3371
 
4.8%
Other values (4) 13188
18.8%

department
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size612.0 KiB
Engineering
2082 
Sales
1992 
Support
1982 
R&D
1972 
HR
1972 

Length

Max length11
Median length5
Mean length5.6596
Min length2

Characters and Unicode

Total characters56596
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSupport
2nd rowSales
3rd rowR&D
4th rowEngineering
5th rowSupport

Common Values

ValueCountFrequency (%)
Engineering 2082
20.8%
Sales 1992
19.9%
Support 1982
19.8%
R&D 1972
19.7%
HR 1972
19.7%

Length

2025-04-14T22:26:05.690657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:26:05.757287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
engineering 2082
20.8%
sales 1992
19.9%
support 1982
19.8%
r&d 1972
19.7%
hr 1972
19.7%

Most occurring characters

ValueCountFrequency (%)
n 6246
 
11.0%
e 6156
 
10.9%
g 4164
 
7.4%
i 4164
 
7.4%
r 4064
 
7.2%
S 3974
 
7.0%
p 3964
 
7.0%
R 3944
 
7.0%
E 2082
 
3.7%
s 1992
 
3.5%
Other values (8) 15846
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56596
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 6246
 
11.0%
e 6156
 
10.9%
g 4164
 
7.4%
i 4164
 
7.4%
r 4064
 
7.2%
S 3974
 
7.0%
p 3964
 
7.0%
R 3944
 
7.0%
E 2082
 
3.7%
s 1992
 
3.5%
Other values (8) 15846
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56596
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 6246
 
11.0%
e 6156
 
10.9%
g 4164
 
7.4%
i 4164
 
7.4%
r 4064
 
7.2%
S 3974
 
7.0%
p 3964
 
7.0%
R 3944
 
7.0%
E 2082
 
3.7%
s 1992
 
3.5%
Other values (8) 15846
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56596
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 6246
 
11.0%
e 6156
 
10.9%
g 4164
 
7.4%
i 4164
 
7.4%
r 4064
 
7.2%
S 3974
 
7.0%
p 3964
 
7.0%
R 3944
 
7.0%
E 2082
 
3.7%
s 1992
 
3.5%
Other values (8) 15846
28.0%

job_role
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size636.8 KiB
Manager
2059 
Executive
2009 
Sales Rep
1984 
Developer
1979 
Analyst
1969 

Length

Max length9
Median length9
Mean length8.1944
Min length7

Characters and Unicode

Total characters81944
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManager
2nd rowAnalyst
3rd rowManager
4th rowExecutive
5th rowSales Rep

Common Values

ValueCountFrequency (%)
Manager 2059
20.6%
Executive 2009
20.1%
Sales Rep 1984
19.8%
Developer 1979
19.8%
Analyst 1969
19.7%

Length

2025-04-14T22:26:05.845780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:26:05.921669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
manager 2059
17.2%
executive 2009
16.8%
sales 1984
16.6%
rep 1984
16.6%
developer 1979
16.5%
analyst 1969
16.4%

Most occurring characters

ValueCountFrequency (%)
e 15982
19.5%
a 8071
 
9.8%
l 5932
 
7.2%
r 4038
 
4.9%
n 4028
 
4.9%
v 3988
 
4.9%
t 3978
 
4.9%
p 3963
 
4.8%
s 3953
 
4.8%
M 2059
 
2.5%
Other values (13) 25952
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81944
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 15982
19.5%
a 8071
 
9.8%
l 5932
 
7.2%
r 4038
 
4.9%
n 4028
 
4.9%
v 3988
 
4.9%
t 3978
 
4.9%
p 3963
 
4.8%
s 3953
 
4.8%
M 2059
 
2.5%
Other values (13) 25952
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81944
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 15982
19.5%
a 8071
 
9.8%
l 5932
 
7.2%
r 4038
 
4.9%
n 4028
 
4.9%
v 3988
 
4.9%
t 3978
 
4.9%
p 3963
 
4.8%
s 3953
 
4.8%
M 2059
 
2.5%
Other values (13) 25952
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81944
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 15982
19.5%
a 8071
 
9.8%
l 5932
 
7.2%
r 4038
 
4.9%
n 4028
 
4.9%
v 3988
 
4.9%
t 3978
 
4.9%
p 3963
 
4.8%
s 3953
 
4.8%
M 2059
 
2.5%
Other values (13) 25952
31.7%

education_level
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
3
4007 
4
3007 
2
1515 
5
1013 
1
458 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row2
4th row3
5th row5

Common Values

ValueCountFrequency (%)
3 4007
40.1%
4 3007
30.1%
2 1515
 
15.2%
5 1013
 
10.1%
1 458
 
4.6%

Length

2025-04-14T22:26:05.999966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:26:06.065586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 4007
40.1%
4 3007
30.1%
2 1515
 
15.2%
5 1013
 
10.1%
1 458
 
4.6%

Most occurring characters

ValueCountFrequency (%)
3 4007
40.1%
4 3007
30.1%
2 1515
 
15.2%
5 1013
 
10.1%
1 458
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 4007
40.1%
4 3007
30.1%
2 1515
 
15.2%
5 1013
 
10.1%
1 458
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 4007
40.1%
4 3007
30.1%
2 1515
 
15.2%
5 1013
 
10.1%
1 458
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 4007
40.1%
4 3007
30.1%
2 1515
 
15.2%
5 1013
 
10.1%
1 458
 
4.6%

monthly_income
Real number (ℝ)

Distinct9918
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5019.6245
Minimum-224.15
Maximum10212.86
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size78.2 KiB
2025-04-14T22:26:06.150247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-224.15
5-th percentile2555.3265
Q14008.4575
median5017.24
Q36039.9675
95-th percentile7470.7545
Maximum10212.86
Range10437.01
Interquartile range (IQR)2031.51

Descriptive statistics

Standard deviation1496.9021
Coefficient of variation (CV)0.29820998
Kurtosis-0.059510813
Mean5019.6245
Median Absolute Deviation (MAD)1016.235
Skewness0.014884414
Sum50196245
Variance2240715.9
MonotonicityNot monotonic
2025-04-14T22:26:06.245466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5622.16 3
 
< 0.1%
5628.84 2
 
< 0.1%
4460.78 2
 
< 0.1%
2457.04 2
 
< 0.1%
3740.54 2
 
< 0.1%
4334.36 2
 
< 0.1%
5340.78 2
 
< 0.1%
5691.41 2
 
< 0.1%
5246.29 2
 
< 0.1%
4042.62 2
 
< 0.1%
Other values (9908) 9979
99.8%
ValueCountFrequency (%)
-224.15 1
< 0.1%
-203.37 1
< 0.1%
96.17 1
< 0.1%
130.65 1
< 0.1%
135.52 1
< 0.1%
192.07 1
< 0.1%
288.22 1
< 0.1%
339.4 1
< 0.1%
363.57 1
< 0.1%
495.34 1
< 0.1%
ValueCountFrequency (%)
10212.86 1
< 0.1%
10107.81 1
< 0.1%
10095.77 1
< 0.1%
10061.98 1
< 0.1%
9970.48 1
< 0.1%
9886.61 1
< 0.1%
9822.77 1
< 0.1%
9762.81 1
< 0.1%
9741.42 1
< 0.1%
9725.2 1
< 0.1%

total_working_years
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.6175
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:26:06.327439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median18
Q326
95-th percentile33
Maximum34
Range33
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.8739933
Coefficient of variation (CV)0.56046506
Kurtosis-1.2136007
Mean17.6175
Median Absolute Deviation (MAD)9
Skewness-0.020124575
Sum176175
Variance97.495743
MonotonicityNot monotonic
2025-04-14T22:26:06.414022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
33 331
 
3.3%
1 328
 
3.3%
27 318
 
3.2%
17 315
 
3.1%
29 312
 
3.1%
30 308
 
3.1%
12 308
 
3.1%
15 304
 
3.0%
31 303
 
3.0%
25 303
 
3.0%
Other values (24) 6870
68.7%
ValueCountFrequency (%)
1 328
3.3%
2 296
3.0%
3 275
2.8%
4 274
2.7%
5 295
2.9%
6 283
2.8%
7 292
2.9%
8 287
2.9%
9 287
2.9%
10 299
3.0%
ValueCountFrequency (%)
34 288
2.9%
33 331
3.3%
32 284
2.8%
31 303
3.0%
30 308
3.1%
29 312
3.1%
28 290
2.9%
27 318
3.2%
26 301
3.0%
25 303
3.0%

years_at_company
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.507
Minimum0
Maximum19
Zeros505
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:26:06.491262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q314
95-th percentile18
Maximum19
Range19
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.7518918
Coefficient of variation (CV)0.60501649
Kurtosis-1.1934499
Mean9.507
Median Absolute Deviation (MAD)5
Skewness-0.0059716692
Sum95070
Variance33.084259
MonotonicityNot monotonic
2025-04-14T22:26:06.568431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
8 535
 
5.3%
1 516
 
5.2%
10 513
 
5.1%
13 510
 
5.1%
16 510
 
5.1%
4 506
 
5.1%
0 505
 
5.1%
11 504
 
5.0%
9 502
 
5.0%
12 502
 
5.0%
Other values (10) 4897
49.0%
ValueCountFrequency (%)
0 505
5.1%
1 516
5.2%
2 474
4.7%
3 481
4.8%
4 506
5.1%
5 477
4.8%
6 502
5.0%
7 496
5.0%
8 535
5.3%
9 502
5.0%
ValueCountFrequency (%)
19 493
4.9%
18 498
5.0%
17 496
5.0%
16 510
5.1%
15 501
5.0%
14 479
4.8%
13 510
5.1%
12 502
5.0%
11 504
5.0%
10 513
5.1%

years_in_current_role
Real number (ℝ)

High correlation  Zeros 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0901
Minimum0
Maximum14
Zeros643
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:26:06.639835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q311
95-th percentile14
Maximum14
Range14
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.3342659
Coefficient of variation (CV)0.61131237
Kurtosis-1.2209426
Mean7.0901
Median Absolute Deviation (MAD)4
Skewness-0.019948058
Sum70901
Variance18.785861
MonotonicityNot monotonic
2025-04-14T22:26:06.712184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11 704
 
7.0%
14 703
 
7.0%
7 690
 
6.9%
12 690
 
6.9%
13 683
 
6.8%
4 676
 
6.8%
10 665
 
6.7%
5 662
 
6.6%
2 655
 
6.6%
1 652
 
6.5%
Other values (5) 3220
32.2%
ValueCountFrequency (%)
0 643
6.4%
1 652
6.5%
2 655
6.6%
3 652
6.5%
4 676
6.8%
5 662
6.6%
6 652
6.5%
7 690
6.9%
8 625
6.2%
9 648
6.5%
ValueCountFrequency (%)
14 703
7.0%
13 683
6.8%
12 690
6.9%
11 704
7.0%
10 665
6.7%
9 648
6.5%
8 625
6.2%
7 690
6.9%
6 652
6.5%
5 662
6.6%

num_companies_worked
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9815
Minimum0
Maximum12
Zeros1340
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:26:06.785908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3944003
Coefficient of variation (CV)0.70370946
Kurtosis0.81302966
Mean1.9815
Median Absolute Deviation (MAD)1
Skewness0.74181414
Sum19815
Variance1.9443522
MonotonicityNot monotonic
2025-04-14T22:26:06.859448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 2795
28.0%
1 2721
27.2%
3 1763
17.6%
0 1340
13.4%
4 906
 
9.1%
5 321
 
3.2%
6 109
 
1.1%
7 37
 
0.4%
9 3
 
< 0.1%
8 3
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 1340
13.4%
1 2721
27.2%
2 2795
28.0%
3 1763
17.6%
4 906
 
9.1%
5 321
 
3.2%
6 109
 
1.1%
7 37
 
0.4%
8 3
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
10 1
 
< 0.1%
9 3
 
< 0.1%
8 3
 
< 0.1%
7 37
 
0.4%
6 109
 
1.1%
5 321
 
3.2%
4 906
 
9.1%
3 1763
17.6%
2 2795
28.0%

overtime
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
0
6575 
1
3425 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6575
65.8%
1 3425
34.2%

Length

2025-04-14T22:26:06.935315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:26:06.993847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6575
65.8%
1 3425
34.2%

Most occurring characters

ValueCountFrequency (%)
0 6575
65.8%
1 3425
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6575
65.8%
1 3425
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6575
65.8%
1 3425
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6575
65.8%
1 3425
34.2%

job_satisfaction
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
3
3962 
2
2934 
4
2076 
1
1028 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 3962
39.6%
2 2934
29.3%
4 2076
20.8%
1 1028
 
10.3%

Length

2025-04-14T22:26:07.059393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:26:07.122822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 3962
39.6%
2 2934
29.3%
4 2076
20.8%
1 1028
 
10.3%

Most occurring characters

ValueCountFrequency (%)
3 3962
39.6%
2 2934
29.3%
4 2076
20.8%
1 1028
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 3962
39.6%
2 2934
29.3%
4 2076
20.8%
1 1028
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 3962
39.6%
2 2934
29.3%
4 2076
20.8%
1 1028
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 3962
39.6%
2 2934
29.3%
4 2076
20.8%
1 1028
 
10.3%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
3
5496 
4
2046 
2
2016 
1
 
442

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row4
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3 5496
55.0%
4 2046
 
20.5%
2 2016
 
20.2%
1 442
 
4.4%

Length

2025-04-14T22:26:07.195857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:26:07.259462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 5496
55.0%
4 2046
 
20.5%
2 2016
 
20.2%
1 442
 
4.4%

Most occurring characters

ValueCountFrequency (%)
3 5496
55.0%
4 2046
 
20.5%
2 2016
 
20.2%
1 442
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 5496
55.0%
4 2046
 
20.5%
2 2016
 
20.2%
1 442
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 5496
55.0%
4 2046
 
20.5%
2 2016
 
20.2%
1 442
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 5496
55.0%
4 2046
 
20.5%
2 2016
 
20.2%
1 442
 
4.4%

distance_from_home
Real number (ℝ)

High correlation 

Distinct527
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.11873
Minimum0
Maximum88.2
Zeros41
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:26:07.339466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q12.9
median7
Q313.9
95-th percentile30.205
Maximum88.2
Range88.2
Interquartile range (IQR)11

Descriptive statistics

Standard deviation10.135414
Coefficient of variation (CV)1.0016488
Kurtosis5.3581021
Mean10.11873
Median Absolute Deviation (MAD)4.9
Skewness1.9715066
Sum101187.3
Variance102.72662
MonotonicityNot monotonic
2025-04-14T22:26:07.430332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 100
 
1.0%
1.4 100
 
1.0%
2.1 100
 
1.0%
0.4 97
 
1.0%
0.1 96
 
1.0%
2.5 96
 
1.0%
1.5 95
 
0.9%
1 94
 
0.9%
0.6 94
 
0.9%
0.9 92
 
0.9%
Other values (517) 9036
90.4%
ValueCountFrequency (%)
0 41
0.4%
0.1 96
1.0%
0.2 84
0.8%
0.3 100
1.0%
0.4 97
1.0%
0.5 77
0.8%
0.6 94
0.9%
0.7 87
0.9%
0.8 88
0.9%
0.9 92
0.9%
ValueCountFrequency (%)
88.2 1
< 0.1%
79.9 1
< 0.1%
74.7 1
< 0.1%
73.6 1
< 0.1%
70.9 1
< 0.1%
70.6 1
< 0.1%
70.1 1
< 0.1%
69.6 1
< 0.1%
67.6 1
< 0.1%
67.5 1
< 0.1%

training_times_last_year
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4975
Minimum0
Maximum5
Zeros1676
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:26:07.503800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7086503
Coefficient of variation (CV)0.68414425
Kurtosis-1.2752213
Mean2.4975
Median Absolute Deviation (MAD)2
Skewness-0.0047586897
Sum24975
Variance2.9194857
MonotonicityNot monotonic
2025-04-14T22:26:07.573960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 1706
17.1%
1 1686
16.9%
0 1676
16.8%
3 1675
16.8%
5 1642
16.4%
2 1615
16.2%
ValueCountFrequency (%)
0 1676
16.8%
1 1686
16.9%
2 1615
16.2%
3 1675
16.8%
4 1706
17.1%
5 1642
16.4%
ValueCountFrequency (%)
5 1642
16.4%
4 1706
17.1%
3 1675
16.8%
2 1615
16.2%
1 1686
16.9%
0 1676
16.8%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
3
6919 
4
2098 
2
892 
1
 
91

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 6919
69.2%
4 2098
 
21.0%
2 892
 
8.9%
1 91
 
0.9%

Length

2025-04-14T22:26:07.647015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:26:07.711038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 6919
69.2%
4 2098
 
21.0%
2 892
 
8.9%
1 91
 
0.9%

Most occurring characters

ValueCountFrequency (%)
3 6919
69.2%
4 2098
 
21.0%
2 892
 
8.9%
1 91
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 6919
69.2%
4 2098
 
21.0%
2 892
 
8.9%
1 91
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 6919
69.2%
4 2098
 
21.0%
2 892
 
8.9%
1 91
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 6919
69.2%
4 2098
 
21.0%
2 892
 
8.9%
1 91
 
0.9%

attrition
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
0
8410 
1
1590 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8410
84.1%
1 1590
 
15.9%

Length

2025-04-14T22:26:07.781435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:26:07.840581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 8410
84.1%
1 1590
 
15.9%

Most occurring characters

ValueCountFrequency (%)
0 8410
84.1%
1 1590
 
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8410
84.1%
1 1590
 
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8410
84.1%
1 1590
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8410
84.1%
1 1590
 
15.9%

distance_from_home_log
Real number (ℝ)

High correlation 

Distinct527
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0257287
Minimum0
Maximum4.490881
Zeros41
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:26:07.914226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.47000363
Q11.3609766
median2.0794415
Q32.7013612
95-th percentile3.4405781
Maximum4.490881
Range4.490881
Interquartile range (IQR)1.3403847

Descriptive statistics

Standard deviation0.90921606
Coefficient of variation (CV)0.44883407
Kurtosis-0.65691905
Mean2.0257287
Median Absolute Deviation (MAD)0.66845457
Skewness-0.13225938
Sum20257.287
Variance0.82667385
MonotonicityNot monotonic
2025-04-14T22:26:08.004147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2623642645 100
 
1.0%
0.8754687374 100
 
1.0%
1.131402111 100
 
1.0%
0.3364722366 97
 
1.0%
0.0953101798 96
 
1.0%
1.252762968 96
 
1.0%
0.9162907319 95
 
0.9%
0.6931471806 94
 
0.9%
0.4700036292 94
 
0.9%
0.6418538862 92
 
0.9%
Other values (517) 9036
90.4%
ValueCountFrequency (%)
0 41
0.4%
0.0953101798 96
1.0%
0.1823215568 84
0.8%
0.2623642645 100
1.0%
0.3364722366 97
1.0%
0.4054651081 77
0.8%
0.4700036292 94
0.9%
0.5306282511 87
0.9%
0.5877866649 88
0.9%
0.6418538862 92
0.9%
ValueCountFrequency (%)
4.49088104 1
< 0.1%
4.393213824 1
< 0.1%
4.32677816 1
< 0.1%
4.312140507 1
< 0.1%
4.275276265 1
< 0.1%
4.271095074 1
< 0.1%
4.264087337 1
< 0.1%
4.257030144 1
< 0.1%
4.228292535 1
< 0.1%
4.226833745 1
< 0.1%

tenure_performance
Real number (ℝ)

High correlation  Zeros 

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.5263
Minimum0
Maximum76
Zeros505
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:26:08.090935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median28
Q345
95-th percentile60
Maximum76
Range76
Interquartile range (IQR)31

Descriptive statistics

Standard deviation19.003821
Coefficient of variation (CV)0.64362352
Kurtosis-0.79747792
Mean29.5263
Median Absolute Deviation (MAD)16
Skewness0.27186752
Sum295263
Variance361.14522
MonotonicityNot monotonic
2025-04-14T22:26:08.184924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
24 527
 
5.3%
0 505
 
5.1%
36 492
 
4.9%
12 472
 
4.7%
48 449
 
4.5%
18 415
 
4.2%
30 400
 
4.0%
6 392
 
3.9%
39 366
 
3.7%
3 362
 
3.6%
Other values (39) 5620
56.2%
ValueCountFrequency (%)
0 505
5.1%
1 7
 
0.1%
2 52
 
0.5%
3 362
3.6%
4 151
 
1.5%
5 3
 
< 0.1%
6 392
3.9%
7 1
 
< 0.1%
8 140
 
1.4%
9 335
3.4%
ValueCountFrequency (%)
76 113
 
1.1%
72 110
 
1.1%
68 98
 
1.0%
64 120
 
1.2%
60 98
 
1.0%
57 338
3.4%
56 111
 
1.1%
54 336
3.4%
52 99
 
1.0%
51 355
3.5%

experience_gap
Real number (ℝ)

High correlation  Zeros 

Distinct53
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1105
Minimum-18
Maximum34
Zeros256
Zeros (%)2.6%
Negative2535
Negative (%)25.4%
Memory size78.2 KiB
2025-04-14T22:26:08.277982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-18
5-th percentile-11
Q1-1
median8
Q317
95-th percentile27
Maximum34
Range52
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.49543
Coefficient of variation (CV)1.4173515
Kurtosis-0.7538221
Mean8.1105
Median Absolute Deviation (MAD)9
Skewness-0.02443839
Sum81105
Variance132.1449
MonotonicityNot monotonic
2025-04-14T22:26:08.373707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 329
 
3.3%
7 321
 
3.2%
15 317
 
3.2%
9 301
 
3.0%
14 299
 
3.0%
6 298
 
3.0%
12 293
 
2.9%
5 293
 
2.9%
8 290
 
2.9%
11 289
 
2.9%
Other values (43) 6970
69.7%
ValueCountFrequency (%)
-18 13
 
0.1%
-17 31
 
0.3%
-16 49
 
0.5%
-15 61
0.6%
-14 89
0.9%
-13 74
0.7%
-12 108
1.1%
-11 133
1.3%
-10 141
1.4%
-9 146
1.5%
ValueCountFrequency (%)
34 12
 
0.1%
33 30
 
0.3%
32 55
0.5%
31 59
0.6%
30 98
1.0%
29 84
0.8%
28 102
1.0%
27 123
1.2%
26 133
1.3%
25 129
1.3%

avg_tenure_per_company
Real number (ℝ)

High correlation 

Distinct175
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6064885
Minimum0.125
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:26:08.463640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.125
5-th percentile0.66666667
Q13
median6
Q310
95-th percentile22
Maximum34
Range33.875
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.5443508
Coefficient of variation (CV)0.86036425
Kurtosis3.4635817
Mean7.6064885
Median Absolute Deviation (MAD)3.3333333
Skewness1.7366979
Sum76064.885
Variance42.828528
MonotonicityNot monotonic
2025-04-14T22:26:08.559931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 319
 
3.2%
5 312
 
3.1%
4 294
 
2.9%
6 291
 
2.9%
1 270
 
2.7%
2 268
 
2.7%
8 232
 
2.3%
7 223
 
2.2%
11 220
 
2.2%
9 206
 
2.1%
Other values (165) 7365
73.7%
ValueCountFrequency (%)
0.125 1
 
< 0.1%
0.1428571429 4
 
< 0.1%
0.1666666667 12
 
0.1%
0.2 32
 
0.3%
0.2222222222 1
 
< 0.1%
0.25 55
0.5%
0.2857142857 3
 
< 0.1%
0.3333333333 91
0.9%
0.375 2
 
< 0.1%
0.4 43
0.4%
ValueCountFrequency (%)
34 53
0.5%
33 40
0.4%
32 31
0.3%
31 30
0.3%
30 40
0.4%
29 39
0.4%
28 36
0.4%
27 40
0.4%
26 46
0.5%
25 40
0.4%

pca1
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.6342483 × 10-17
Minimum-3.7961486
Maximum3.6374665
Zeros0
Zeros (%)0.0%
Negative5017
Negative (%)50.2%
Memory size78.2 KiB
2025-04-14T22:26:08.655688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.7961486
5-th percentile-1.6665476
Q1-0.70810502
median-0.0040899043
Q30.7045586
95-th percentile1.6910459
Maximum3.6374665
Range7.433615
Interquartile range (IQR)1.4126636

Descriptive statistics

Standard deviation1.0289558
Coefficient of variation (CV)-6.2962024 × 1016
Kurtosis-0.155143
Mean-1.6342483 × 10-17
Median Absolute Deviation (MAD)0.70606054
Skewness0.0030310034
Sum-1.5987212 × 10-13
Variance1.0587501
MonotonicityNot monotonic
2025-04-14T22:26:08.754041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2474213527 1
 
< 0.1%
0.7823583184 1
 
< 0.1%
1.054449514 1
 
< 0.1%
0.2973639218 1
 
< 0.1%
0.3137150654 1
 
< 0.1%
-0.7385836143 1
 
< 0.1%
2.496671171 1
 
< 0.1%
0.1669680508 1
 
< 0.1%
-1.800204791 1
 
< 0.1%
1.899862702 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-3.796148554 1
< 0.1%
-3.429560772 1
< 0.1%
-3.309436665 1
< 0.1%
-3.274746723 1
< 0.1%
-3.245677396 1
< 0.1%
-3.238190102 1
< 0.1%
-3.199655943 1
< 0.1%
-3.198054485 1
< 0.1%
-3.191654215 1
< 0.1%
-3.140105791 1
< 0.1%
ValueCountFrequency (%)
3.637466486 1
< 0.1%
3.524817228 1
< 0.1%
3.467705328 1
< 0.1%
3.374603453 1
< 0.1%
3.287208771 1
< 0.1%
3.278207172 1
< 0.1%
3.185162503 1
< 0.1%
3.121905406 1
< 0.1%
3.091554285 1
< 0.1%
3.087564503 1
< 0.1%

pca2
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8014036 × 10-17
Minimum-3.4636841
Maximum3.9346319
Zeros0
Zeros (%)0.0%
Negative5032
Negative (%)50.3%
Memory size78.2 KiB
2025-04-14T22:26:08.848922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4636841
5-th percentile-1.6928786
Q1-0.68935624
median-0.0086090218
Q30.69354946
95-th percentile1.7095201
Maximum3.9346319
Range7.3983161
Interquartile range (IQR)1.3829057

Descriptive statistics

Standard deviation1.0231942
Coefficient of variation (CV)2.691622 × 1016
Kurtosis-0.091811025
Mean3.8014036 × 10-17
Median Absolute Deviation (MAD)0.69245082
Skewness0.011419816
Sum3.4106051 × 10-13
Variance1.0469263
MonotonicityNot monotonic
2025-04-14T22:26:08.943855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.939056021 1
 
< 0.1%
0.1790139373 1
 
< 0.1%
1.664401056 1
 
< 0.1%
0.481262743 1
 
< 0.1%
0.5106427067 1
 
< 0.1%
-0.03907583187 1
 
< 0.1%
-1.202204776 1
 
< 0.1%
-1.085109523 1
 
< 0.1%
-1.237064198 1
 
< 0.1%
0.3931493703 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-3.463684148 1
< 0.1%
-3.328001543 1
< 0.1%
-3.308181971 1
< 0.1%
-3.232301533 1
< 0.1%
-3.201929968 1
< 0.1%
-3.190985338 1
< 0.1%
-3.102656786 1
< 0.1%
-3.051935249 1
< 0.1%
-3.025864326 1
< 0.1%
-3.024531738 1
< 0.1%
ValueCountFrequency (%)
3.934631924 1
< 0.1%
3.733525985 1
< 0.1%
3.609594872 1
< 0.1%
3.556951154 1
< 0.1%
3.320741271 1
< 0.1%
3.272249718 1
< 0.1%
3.225948695 1
< 0.1%
3.12899391 1
< 0.1%
3.116839332 1
< 0.1%
3.04433365 1
< 0.1%

cluster
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
1
3386 
3
2478 
2
2244 
0
1892 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 3386
33.9%
3 2478
24.8%
2 2244
22.4%
0 1892
18.9%

Length

2025-04-14T22:26:09.231555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:26:09.297314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3386
33.9%
3 2478
24.8%
2 2244
22.4%
0 1892
18.9%

Most occurring characters

ValueCountFrequency (%)
1 3386
33.9%
3 2478
24.8%
2 2244
22.4%
0 1892
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3386
33.9%
3 2478
24.8%
2 2244
22.4%
0 1892
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3386
33.9%
3 2478
24.8%
2 2244
22.4%
0 1892
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3386
33.9%
3 2478
24.8%
2 2244
22.4%
0 1892
18.9%

Interactions

2025-04-14T22:26:02.851522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:48.910213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:50.880796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:51.826283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:52.881138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:53.868897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:54.951624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:55.908850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:56.943904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:57.873905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:58.832536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:59.803151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:00.896437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:01.878125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:02.919416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:49.013250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:50.950478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:51.895840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:52.953641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:53.942706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:55.022540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:55.977377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:57.011883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:57.941677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:58.903198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:59.870611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:00.967909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:01.948847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:02.986010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:49.115014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:51.014583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:51.964245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:53.022199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:54.009739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:55.089166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:56.042332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:57.076802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:58.004473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:58.969716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:00.073365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:01.035342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:02.017336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:03.056672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:49.206441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:51.083756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:52.034628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:53.094207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:54.081911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:55.160654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:56.109855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:57.144136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:58.072067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:59.041566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:00.142202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:01.107254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:02.087762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:03.127571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:49.279690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:51.154709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:52.186681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:53.166484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:54.154489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:55.231886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:56.178920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:57.214402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:58.140931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:59.113773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:00.212826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:01.180718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:02.160803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:03.198354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:49.350436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:51.224273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:52.259742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:53.239211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:54.315274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:55.301004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:56.246796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:57.282827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:58.208160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:59.185449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:00.280645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:01.254766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:02.232624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:03.267520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:49.420248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:51.292532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:52.329378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:53.308062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:54.397158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:55.368096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:56.311779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:57.348428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:58.274279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:59.254648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:00.348163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:01.324799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:02.301336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:03.330347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:49.483537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:51.354833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:52.394771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:53.374180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:54.461457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:55.430803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:56.371586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:57.411436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:58.368950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:59.319123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:00.411626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:01.390517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:02.366520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:03.396759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:50.456279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:51.420684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:52.461478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:53.441645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:54.529942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:55.497910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:56.435805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:57.474272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:58.432057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:59.386442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:00.476002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:01.459139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:02.433656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:03.459554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:50.526447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:51.482311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:52.525241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:53.508215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:54.593258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:55.559637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:56.497742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:57.535025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:58.491876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:59.451581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:00.536873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:01.523406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:02.498212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:03.529909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:50.600639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:51.552295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:52.598284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:53.581265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:54.665591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:55.630348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:56.565402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:57.603735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:58.560009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:59.521535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:00.605968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:01.596228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:02.569932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:03.594438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:50.666564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:51.617033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:52.665748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:53.649677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:54.733578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:55.696582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:56.627124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:57.667592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:58.622789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:59.587186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:00.667985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:01.662237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:02.636968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:03.666303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:50.737771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:51.686530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:52.737339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:53.722374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:54.805086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:55.767010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:56.803901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:57.737353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:58.692231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:59.659427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:00.735695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:01.733625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:02.709285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:03.905361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:50.810556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:51.756880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:52.809810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:53.796498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:54.879152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:55.838942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:56.875384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:57.806449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:58.760633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:59.731834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:00.826681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:01.806590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:26:02.780761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-14T22:26:09.372939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ageattritionavg_tenure_per_companyclusterdepartmentdistance_from_homedistance_from_home_logeducation_levelexperience_gapgenderjob_rolejob_satisfactionmarital_statusmonthly_incomenum_companies_workedovertimepca1pca2performance_ratingtenure_performancetotal_working_yearstraining_times_last_yearwork_life_balanceyears_at_companyyears_in_current_role
age1.0000.020-0.0040.3880.0180.0080.0080.000-0.0070.0080.0210.0000.000-0.0110.0050.000-0.220-0.2780.0000.0170.0000.0180.0000.017-0.002
attrition0.0201.0000.0000.0030.0000.0000.0000.0240.0200.0070.0000.0080.0000.0000.0210.0000.0130.0230.0000.0000.0000.0000.0000.0000.000
avg_tenure_per_company-0.0040.0001.0000.1190.000-0.004-0.0040.0050.6910.0200.0200.0000.006-0.011-0.5450.032-0.2510.3000.015-0.0180.7890.0040.012-0.019-0.003
cluster0.3880.0030.1191.0000.0000.0400.0450.1050.2150.0000.0140.2090.0110.0520.0220.9910.1060.2710.0910.2060.1430.0090.1070.1910.000
department0.0180.0000.0000.0001.0000.0000.0060.0000.0130.0000.0160.0140.0020.0000.0100.0090.0000.0090.0110.0170.0080.0050.0050.0100.000
distance_from_home0.0080.000-0.0040.0400.0001.0001.0000.009-0.0070.0000.0000.0000.0000.001-0.0020.0000.017-0.2440.0120.005-0.0050.0040.0100.004-0.004
distance_from_home_log0.0080.000-0.0040.0450.0061.0001.0000.011-0.0070.0000.0000.0000.0000.001-0.0020.0120.017-0.2440.0110.005-0.0050.0040.0050.004-0.004
education_level0.0000.0240.0050.1050.0000.0090.0111.0000.0000.0000.0000.0070.0000.0070.0080.0200.1690.1330.0100.0150.0000.0000.0130.0170.008
experience_gap-0.0070.0200.6910.2150.013-0.007-0.0070.0001.0000.0060.0000.0150.000-0.0140.0020.018-0.3810.5320.017-0.4670.872-0.0020.011-0.489-0.013
gender0.0080.0070.0200.0000.0000.0000.0000.0000.0061.0000.0090.0000.0070.0000.0000.0000.0000.0000.0090.0000.0000.0130.0080.0000.013
job_role0.0210.0000.0200.0140.0160.0000.0000.0000.0000.0091.0000.0000.0050.0090.0000.0070.0000.0160.0110.0000.0000.0030.0000.0000.004
job_satisfaction0.0000.0080.0000.2090.0140.0000.0000.0070.0150.0000.0001.0000.0080.0000.0050.0000.1760.0240.0080.0000.0000.0130.0000.0000.000
marital_status0.0000.0000.0060.0110.0020.0000.0000.0000.0000.0070.0050.0081.0000.0000.0060.0000.0190.0000.0000.0000.0070.0200.0000.0100.000
monthly_income-0.0110.000-0.0110.0520.0000.0010.0010.007-0.0140.0000.0090.0000.0001.000-0.0010.0000.3430.1230.0090.006-0.012-0.0020.0140.0050.027
num_companies_worked0.0050.021-0.5450.0220.010-0.002-0.0020.0080.0020.0000.0000.0050.006-0.0011.0000.0000.0150.1600.0000.0080.008-0.0120.0240.012-0.006
overtime0.0000.0000.0320.9910.0090.0000.0120.0200.0180.0000.0070.0000.0000.0000.0001.0000.0000.2520.0080.0310.0000.0000.0000.0320.000
pca1-0.2200.013-0.2510.1060.0000.0170.0170.169-0.3810.0000.0000.1760.0190.3430.0150.0001.0000.0030.1370.180-0.302-0.3130.1210.2460.574
pca2-0.2780.0230.3000.2710.009-0.244-0.2440.1330.5320.0000.0160.0240.0000.1230.1600.2520.0031.0000.117-0.2690.486-0.3540.286-0.2280.179
performance_rating0.0000.0000.0150.0910.0110.0120.0110.0100.0170.0090.0110.0080.0000.0090.0000.0080.1370.1171.0000.2840.0000.0000.0100.0000.004
tenure_performance0.0170.000-0.0180.2060.0170.0050.0050.015-0.4670.0000.0000.0000.0000.0060.0080.0310.180-0.2690.2841.000-0.0140.0020.0000.9540.012
total_working_years0.0000.0000.7890.1430.008-0.005-0.0050.0000.8720.0000.0000.0000.007-0.0120.0080.000-0.3020.4860.000-0.0141.000-0.0010.018-0.014-0.005
training_times_last_year0.0180.0000.0040.0090.0050.0040.0040.000-0.0020.0130.0030.0130.020-0.002-0.0120.000-0.313-0.3540.0000.002-0.0011.0000.0000.001-0.024
work_life_balance0.0000.0000.0120.1070.0050.0100.0050.0130.0110.0080.0000.0000.0000.0140.0240.0000.1210.2860.0100.0000.0180.0001.0000.0000.000
years_at_company0.0170.000-0.0190.1910.0100.0040.0040.017-0.4890.0000.0000.0000.0100.0050.0120.0320.246-0.2280.0000.954-0.0140.0010.0001.0000.018
years_in_current_role-0.0020.000-0.0030.0000.000-0.004-0.0040.008-0.0130.0130.0040.0000.0000.027-0.0060.0000.5740.1790.0040.012-0.005-0.0240.0000.0181.000

Missing values

2025-04-14T22:26:04.030410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-14T22:26:04.271390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

employee_idagegendermarital_statusdepartmentjob_roleeducation_levelmonthly_incometotal_working_yearsyears_at_companyyears_in_current_rolenum_companies_workedovertimejob_satisfactionwork_life_balancedistance_from_hometraining_times_last_yearperformance_ratingattritiondistance_from_home_logtenure_performanceexperience_gapavg_tenure_per_companypca1pca2cluster
0EMP0000050MaleDivorcedSupportManager23444.941155102310.45402.43361360-140.500000-0.247421-2.9390563
1EMP0000136MaleDivorcedSalesAnalyst46218.87257021230.45300.33647221188.333333-1.2744110.6972771
2EMP0000229MaleDivorcedR&DManager23298.271941112422.81203.1696868159.5000000.408044-0.1797471
3EMP0000342MaleSingleEngineeringExecutive34197.502105303412.70302.61739630-80.5000000.800571-1.2727352
4EMP0000440OtherSingleSupportSales Rep57033.501210130430.80400.5877874023.000000-0.9512300.3725212
5EMP0000544MaleMarriedR&DExecutive34992.352591350338.81402.28238236164.1666670.4073560.6095383
6EMP0000632MaleSingleSupportManager25691.7632123203353.55313.998201362010.666667-0.561902-1.5567362
7EMP0000732MaleSingleEngineeringManager51938.531810800330.90400.64185440818.000000-0.9739930.4102852
8EMP0000845FemaleSingleHRAnalyst34524.1031181330340.24400.18232272137.7500000.172612-0.7264073
9EMP0000957MaleMarriedHRManager25423.1190210224.45301.686399094.500000-1.101874-0.9824683
employee_idagegendermarital_statusdepartmentjob_roleeducation_levelmonthly_incometotal_working_yearsyears_at_companyyears_in_current_rolenum_companies_workedovertimejob_satisfactionwork_life_balancedistance_from_hometraining_times_last_yearperformance_ratingattritiondistance_from_home_logtenure_performanceexperience_gapavg_tenure_per_companypca1pca2cluster
9990EMP0999047OtherDivorcedEngineeringManager35845.9731116102225.23303.265759332015.500000-0.4130740.1685770
9991EMP0999129FemaleMarriedSupportExecutive16185.231523011328.70303.39114761315.0000001.5229860.0053831
9992EMP0999237FemaleDivorcedEngineeringDeveloper34573.671551220232.55311.25276315105.0000000.386204-0.2128580
9993EMP0999324MaleDivorcedHRAnalyst35551.343491113415.73302.815409272517.000000-0.6091890.3112951
9994EMP0999453FemaleSingleSalesExecutive46080.84556203331.55303.4812401501.666667-0.649392-1.8333013
9995EMP0999526FemaleDivorcedEngineeringDeveloper37349.902216721330.42300.3364724867.3333331.1064301.1473191
9996EMP0999641FemaleDivorcedEngineeringDeveloper45778.501261140337.02302.0794421862.4000000.5316180.4879212
9997EMP0999735FemaleSingleSalesSales Rep34438.86311312003318.53302.970414391831.0000000.287860-0.0481202
9998EMP0999853MaleMarriedSupportSales Rep37822.11291420325.43301.8562983289.666667-1.0372791.0320080
9999EMP0999947FemaleDivorcedHRExecutive46959.132213840126.74302.0412203994.4000000.3007750.8015350